{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "a1921d43",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import sympy as sp\n",
    "import sympy  as sp\n",
    "from sympy.matrices import Matrix\n",
    "from functools import partial\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "from math import pi\n",
    "import pprint\n",
    "pp=pprint.PrettyPrinter(indent=5)\n",
    "\n",
    "x,y,z,r,o=sp.symbols(\"x y z r theta\")\n",
    "#Tool Velocity Matrix\n",
    "\n",
    "\n",
    "X_BR=sp.Matrix([[400*sp.sin((2*np.pi*o)/50)*(2*np.pi)/5],[0],[400*sp.cos((2*np.pi*o)/50)*(2*np.pi)/5],[0],[0],[0]])\n",
    "X1_BR=sp.Matrix([[-320],[0],[0],[0],[0],[0]])\n",
    "X_FR=sp.Matrix([[-160],[0],[0],[0],[0],[0]])\n",
    "X1_FR=sp.Matrix([[160],[0],[0],[0],[0],[0]])\n",
    "X_BL=sp.Matrix([[-160],[0],[0],[0],[0],[0]])\n",
    "X1_BL=sp.Matrix([[160],[0],[0],[0],[0],[0]])\n",
    "X_FL=sp.Matrix([[400*sp.sin((2*np.pi*o)/50)*(2*np.pi)/5],[0],[400*sp.cos((2*np.pi*o)/50)*(2*np.pi)/5],[0],[0],[0]])\n",
    "X1_FL=sp.Matrix([[-320],[0],[0],[0],[0],[0]])\n",
    "\n",
    "\n",
    "file_name='front_jump_gait.json'\n",
    "\n",
    "theta_i, alpha_i, d_i, a_i, A_i, a_3, d_1, d_3, d_5, d_7 = sp.symbols('theta_i alpha_i d_i a_i A_i a_3 d_1, d_3, d_5, d_7')\n",
    "theta_1,theta_2,theta_3,theta_4,theta_5,theta_6,theta_7 = sp.symbols ('theta_1,theta_2, theta_3, theta_4, theta_5, theta_6, theta_7')\n",
    "Rot_z = sp.Matrix([ [sp.cos(theta_i), -sp.sin(theta_i),0,0], [sp.sin(theta_i),sp.cos(theta_i),0,0], [0,0,1,0], [0,0,0,1] ]);\n",
    "Rot_x = sp.Matrix([ [1,0,0,0], [0,sp.cos(alpha_i), -sp.sin(alpha_i),0], [0, sp.sin(alpha_i), sp.cos(alpha_i), 0], [0,0,0,1] ]); \n",
    "Tran_z = sp.Matrix([[1,0,0,0], [0,1,0,0], [0,0,1,d_i], [0,0,0,1]])\n",
    "Tran_x = sp.Matrix([[1,0,0,a_i], [0,1,0,0], [0,0,1,0], [0,0,0,1]])\n",
    "\n",
    "A_i=Rot_z*Tran_z*Tran_x*Rot_x\n",
    "\n",
    "# Back Left Leg\n",
    "\n",
    "BL_Ad0=A_i.subs([(theta_i,math.radians(90)),(alpha_i,math.radians(90)),(a_i,0),(d_i,0)])\n",
    "BL_A1=A_i.subs([(theta_i,theta_1),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "BL_Ad1=A_i.subs([(theta_i,math.radians(-90)),(alpha_i,math.radians(90)),(a_i,0),(d_i,64.85)])\n",
    "BL_A2=A_i.subs([(theta_i,theta_2),(alpha_i,math.radians(0)),(a_i,0),(d_i,-140)])\n",
    "BL_Ad2=A_i.subs([(theta_i,math.radians(0)),(alpha_i,math.radians(0)),(a_i,500),(d_i,0)])\n",
    "BL_A3=A_i.subs([(theta_i,theta_3),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "BL_A4=A_i.subs([(theta_i,0),(alpha_i,math.radians(0)),(a_i,450),(d_i,0)])\n",
    "\n",
    "# Back Right Leg\n",
    "BR_Ad0=A_i.subs([(theta_i,math.radians(-90)),(alpha_i,math.radians(90)),(a_i,0),(d_i,0)])\n",
    "BR_A1=A_i.subs([(theta_i,theta_1),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "BR_Ad1=A_i.subs([(theta_i,math.radians(-90)),(alpha_i,math.radians(-90)),(a_i,0),(d_i,64.85)])\n",
    "BR_A2=A_i.subs([(theta_i,theta_2),(alpha_i,math.radians(0)),(a_i,0),(d_i,140)])\n",
    "BR_Ad2=A_i.subs([(theta_i,math.radians(0)),(alpha_i,math.radians(0)),(a_i,500),(d_i,0)])\n",
    "BR_A3=A_i.subs([(theta_i,theta_3),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "BR_A4=A_i.subs([(theta_i,0),(alpha_i,math.radians(0)),(a_i,450),(d_i,0)])\n",
    "\n",
    "\n",
    "#Front Left Leg\n",
    "FL_Ad0=A_i.subs([(theta_i,math.radians(90)),(alpha_i,math.radians(90)),(a_i,0),(d_i,0)])\n",
    "FL_A1=A_i.subs([(theta_i,theta_1),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "FL_Ad1=A_i.subs([(theta_i,math.radians(-90)),(alpha_i,math.radians(90)),(a_i,0),(d_i,64.85)])\n",
    "FL_A2=A_i.subs([(theta_i,theta_2),(alpha_i,math.radians(0)),(a_i,0),(d_i,-140)])\n",
    "FL_Ad2=A_i.subs([(theta_i,math.radians(0)),(alpha_i,math.radians(0)),(a_i,500),(d_i,0)])\n",
    "FL_A3=A_i.subs([(theta_i,theta_3),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "FL_A4=A_i.subs([(theta_i,0),(alpha_i,math.radians(0)),(a_i,450),(d_i,0)])\n",
    "\n",
    "\n",
    "#Front Right Leg\n",
    "FR_Ad0=A_i.subs([(theta_i,math.radians(-90)),(alpha_i,math.radians(90)),(a_i,0),(d_i,0)])\n",
    "FR_A1=A_i.subs([(theta_i,theta_1),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "FR_Ad1=A_i.subs([(theta_i,math.radians(-90)),(alpha_i,math.radians(-90)),(a_i,0),(d_i,-64.85)])\n",
    "FR_A2=A_i.subs([(theta_i,theta_2),(alpha_i,math.radians(0)),(a_i,0),(d_i,140)])\n",
    "FR_Ad2=A_i.subs([(theta_i,math.radians(0)),(alpha_i,math.radians(0)),(a_i,500),(d_i,0)])\n",
    "FR_A3=A_i.subs([(theta_i,theta_3),(alpha_i,math.radians(0)),(a_i,0),(d_i,0)])\n",
    "FR_A4=A_i.subs([(theta_i,0),(alpha_i,math.radians(0)),(a_i,450),(d_i,0)])\n",
    "\n",
    "\n",
    "T1=BR_Ad0*BR_A1\n",
    "T2=BR_Ad0*BR_A1*BR_Ad1*BR_A2\n",
    "T3=BR_Ad0*BR_A1*BR_Ad1*BR_A2*BR_Ad2*BR_A3\n",
    "T4=BR_Ad0*BR_A1*BR_Ad1*BR_A2*BR_Ad2*BR_A3*BR_A4\n",
    "\n",
    "Z0 = T1[:3,2]\n",
    "Z1 = T2[:3,2]\n",
    "Z2 = T3[:3,2]\n",
    "Z3 = T4[:3,2]\n",
    "Xp=T4[:3,3]\n",
    "diff_thet_1 = Xp.diff(theta_1) #Partially differentiating Xp wrt θ1\n",
    "diff_thet_2 = Xp.diff(theta_2) #Partially differentiating Xp wrt θ2\n",
    "diff_thet_3 = Xp.diff(theta_3) #Partially differentiating Xp wrt θ4\n",
    "J_BR = Matrix([[diff_thet_1[0],diff_thet_2[0],diff_thet_3[0]],\n",
    "          [diff_thet_1[1],diff_thet_2[1],diff_thet_3[1]],\n",
    "          [diff_thet_1[2],diff_thet_2[2],diff_thet_3[2]],\n",
    "          [Z0[0],Z1[0],Z2[0]],[Z0[1],Z1[1],Z2[1]],[Z0[2],Z1[2],Z2[2]]])\n",
    "\n",
    "T1=BL_Ad0*BL_A1\n",
    "T2=BL_Ad0*BL_A1*BL_Ad1*BL_A2\n",
    "T3=BL_Ad0*BL_A1*BL_Ad1*BL_A2*BL_Ad2*BL_A3\n",
    "T4=BL_Ad0*BL_A1*BL_Ad1*BL_A2*BL_Ad2*BL_A3*BL_A4\n",
    "\n",
    "Z0 = T1[:3,2]\n",
    "Z1 = T2[:3,2]\n",
    "Z2 = T3[:3,2]\n",
    "Z3 = T4[:3,2]\n",
    "Xp=T4[:3,3]\n",
    "diff_thet_1 = Xp.diff(theta_1) #Partially differentiating Xp wrt θ1\n",
    "diff_thet_2 = Xp.diff(theta_2) #Partially differentiating Xp wrt θ2\n",
    "diff_thet_3 = Xp.diff(theta_3) #Partially differentiating Xp wrt θ4\n",
    "J_BL = Matrix([[diff_thet_1[0],diff_thet_2[0],diff_thet_3[0]],\n",
    "          [diff_thet_1[1],diff_thet_2[1],diff_thet_3[1]],\n",
    "          [diff_thet_1[2],diff_thet_2[2],diff_thet_3[2]],\n",
    "          [Z0[0],Z1[0],Z2[0]],[Z0[1],Z1[1],Z2[1]],[Z0[2],Z1[2],Z2[2]]])\n",
    "\n",
    "T1=FL_Ad0*FL_A1\n",
    "T2=FL_Ad0*FL_A1*FL_Ad1*FL_A2\n",
    "T3=FL_Ad0*FL_A1*FL_Ad1*FL_A2*FL_Ad2*FL_A3\n",
    "T4=FL_Ad0*FL_A1*FL_Ad1*FL_A2*FL_Ad2*FL_A3*FL_A4\n",
    "\n",
    "Z0 = T1[:3,2]\n",
    "Z1 = T2[:3,2]\n",
    "Z2 = T3[:3,2]\n",
    "Z3 = T4[:3,2]\n",
    "Xp=T4[:3,3]\n",
    "diff_thet_1 = Xp.diff(theta_1) #Partially differentiating Xp wrt θ1\n",
    "diff_thet_2 = Xp.diff(theta_2) #Partially differentiating Xp wrt θ2\n",
    "diff_thet_3 = Xp.diff(theta_3) #Partially differentiating Xp wrt θ4\n",
    "J_FL = Matrix([[diff_thet_1[0],diff_thet_2[0],diff_thet_3[0]],\n",
    "          [diff_thet_1[1],diff_thet_2[1],diff_thet_3[1]],\n",
    "          [diff_thet_1[2],diff_thet_2[2],diff_thet_3[2]],\n",
    "          [Z0[0],Z1[0],Z2[0]],[Z0[1],Z1[1],Z2[1]],[Z0[2],Z1[2],Z2[2]]])\n",
    "\n",
    "T1=FR_Ad0*FR_A1\n",
    "T2=FR_Ad0*FR_A1*FR_Ad1*FR_A2\n",
    "T3=FR_Ad0*FR_A1*FR_Ad1*FR_A2*FR_Ad2*FR_A3\n",
    "T4=FR_Ad0*FR_A1*FR_Ad1*FR_A2*FR_Ad2*FR_A3*FR_A4\n",
    "\n",
    "Z0 = T1[:3,2]\n",
    "Z1 = T2[:3,2]\n",
    "Z2 = T3[:3,2]\n",
    "Z3 = T4[:3,2]\n",
    "Xp=T4[:3,3]\n",
    "diff_thet_1 = Xp.diff(theta_1) #Partially differentiating Xp wrt θ1\n",
    "diff_thet_2 = Xp.diff(theta_2) #Partially differentiating Xp wrt θ2\n",
    "diff_thet_3 = Xp.diff(theta_3) #Partially differentiating Xp wrt θ4\n",
    "J_FR = Matrix([[diff_thet_1[0],diff_thet_2[0],diff_thet_3[0]],\n",
    "          [diff_thet_1[1],diff_thet_2[1],diff_thet_3[1]],\n",
    "          [diff_thet_1[2],diff_thet_2[2],diff_thet_3[2]],\n",
    "          [Z0[0],Z1[0],Z2[0]],[Z0[1],Z1[1],Z2[1]],[Z0[2],Z1[2],Z2[2]]])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "9a72d216",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing Trajectory\n",
      "..................................................."
     ]
    }
   ],
   "source": [
    "dt=0.05  #Time difference\n",
    "t_1=[math.radians(0)]  #Theta 1\n",
    "t_2=[-0.7]  #Theta 2\n",
    "t_3=[1.5]  #Theta 4\n",
    "back_right_joint=[]\n",
    "T=T4\n",
    "x_tool_br=[]\n",
    "y_tool_br=[]\n",
    "z_tool_br=[]\n",
    "#Tool Velocity Matrix\n",
    "# X=sp.Matrix([[100*sp.sin((2*np.pi*o)/200)*(2*np.pi)/10],[0],[100*sp.cos((2*np.pi*o)/200)*(2*np.pi)/10],[0],[0],[0]])\n",
    "# X1=sp.Matrix([[-40],[0],[0],[0],[0],[0]])\n",
    "i=0\n",
    "j=0\n",
    "print(\"Computing Trajectory\")\n",
    "while i<=50:\n",
    "    if i<25:\n",
    "        X_eval=X_BR.subs(o,i)\n",
    "    if i>25:\n",
    "        X_eval=X1_BR.subs(o,j)\n",
    "        j+=1\n",
    "    back_right_joint.append([t_1[i],t_2[i],t_3[i]])\n",
    "    T_eval=T.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    x_tool_br.append(T_eval[3])\n",
    "    y_tool_br.append(T_eval[7])\n",
    "    z_tool_br.append(T_eval[11])\n",
    "    J_eval=J_BR.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    q=J_eval.pinv()*X_eval\n",
    "    q=q*dt\n",
    "#     print(q)\n",
    "    t_1.append(q[0]+t_1[i])\n",
    "    t_2.append(q[1]+t_2[i])\n",
    "    t_3.append(q[2]+t_3[i])\n",
    "    \n",
    "    print(\".\",end=\"\")\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "1e406526",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing Trajectory\n",
      "..................................................."
     ]
    }
   ],
   "source": [
    "dt=0.05  #Time difference\n",
    "t_1=[math.radians(0)]  #Theta 1\n",
    "t_2=[-0.7]  #Theta 2\n",
    "t_3=[1.5]  #Theta 4\n",
    "front_right_joint=[]\n",
    "T=T4\n",
    "x_tool_fr=[]\n",
    "y_tool_fr=[]\n",
    "z_tool_fr=[]\n",
    "#Tool Velocity Matrix\n",
    "# X=sp.Matrix([[100*sp.sin((2*np.pi*o)/200)*(2*np.pi)/10],[0],[100*sp.cos((2*np.pi*o)/200)*(2*np.pi)/10],[0],[0],[0]])\n",
    "# X1=sp.Matrix([[-40],[0],[0],[0],[0],[0]])\n",
    "i=0\n",
    "j=0\n",
    "print(\"Computing Trajectory\")\n",
    "while i<=50:\n",
    "    if i<25:\n",
    "        X_eval=X_FR.subs(o,i)\n",
    "    if i>25:\n",
    "        X_eval=X1_FR.subs(o,j)\n",
    "        j+=1\n",
    "    front_right_joint.append([t_1[i],t_2[i],t_3[i]])\n",
    "    T_eval=T.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    x_tool_fr.append(T_eval[3])\n",
    "    y_tool_fr.append(T_eval[7])\n",
    "    z_tool_fr.append(T_eval[11])\n",
    "    J_eval=J_FR.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    q=J_eval.pinv()*X_eval\n",
    "    q=q*dt\n",
    "#     print(q)\n",
    "    t_1.append(q[0]+t_1[i])\n",
    "    t_2.append(q[1]+t_2[i])\n",
    "    t_3.append(q[2]+t_3[i])\n",
    "    \n",
    "    print(\".\",end=\"\")\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "3d70a9fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing Trajectory\n",
      "..................................................."
     ]
    }
   ],
   "source": [
    "dt=0.05  #Time difference\n",
    "t_1=[math.radians(0)]  #Theta 1\n",
    "t_2=[-0.7]  #Theta 2\n",
    "t_3=[1.5]  #Theta 4\n",
    "front_left_joint=[]\n",
    "T=T4\n",
    "x_tool_fl=[]\n",
    "y_tool_fl=[]\n",
    "z_tool_fl=[]\n",
    "#Tool Velocity Matrix\n",
    "# X=sp.Matrix([[100*sp.sin((2*np.pi*o)/200)*(2*np.pi)/10],[0],[100*sp.cos((2*np.pi*o)/200)*(2*np.pi)/10],[0],[0],[0]])\n",
    "# X1=sp.Matrix([[-40],[0],[0],[0],[0],[0]])\n",
    "i=0\n",
    "j=0\n",
    "print(\"Computing Trajectory\")\n",
    "while i<=50:\n",
    "    if i<25:\n",
    "        X_eval=X_FL.subs(o,i)\n",
    "    if i>25:\n",
    "        X_eval=X1_FL.subs(o,j)\n",
    "        j+=1\n",
    "    front_left_joint.append([t_1[i],t_2[i],t_3[i]])\n",
    "    T_eval=T.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    x_tool_fl.append(T_eval[3])\n",
    "    y_tool_fl.append(T_eval[7])\n",
    "    z_tool_fl.append(T_eval[11])\n",
    "    J_eval=J_FL.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    q=J_eval.pinv()*X_eval\n",
    "    q=q*dt\n",
    "#     print(q)\n",
    "    t_1.append(q[0]+t_1[i])\n",
    "    t_2.append(q[1]+t_2[i])\n",
    "    t_3.append(q[2]+t_3[i])\n",
    "    \n",
    "    print(\".\",end=\"\")\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "8f30a51c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing Trajectory\n",
      "..................................................."
     ]
    }
   ],
   "source": [
    "dt=0.05  #Time difference\n",
    "t_1=[math.radians(0)]  #Theta 1\n",
    "t_2=[-0.7]  #Theta 2\n",
    "t_3=[1.5]  #Theta 4\n",
    "back_left_joint=[]\n",
    "T=T4\n",
    "x_tool_bl=[]\n",
    "y_tool_bl=[]\n",
    "z_tool_bl=[]\n",
    "#Tool Velocity Matrix\n",
    "# X=sp.Matrix([[100*sp.sin((2*np.pi*o)/200)*(2*np.pi)/10],[0],[100*sp.cos((2*np.pi*o)/200)*(2*np.pi)/10],[0],[0],[0]])\n",
    "# X1=sp.Matrix([[-40],[0],[0],[0],[0],[0]])\n",
    "i=0\n",
    "j=0\n",
    "print(\"Computing Trajectory\")\n",
    "while i<=50:\n",
    "    if i<25:\n",
    "        X_eval=X_BL.subs(o,i)\n",
    "    if i>25:\n",
    "        X_eval=X1_BL.subs(o,j)\n",
    "        j+=1\n",
    "    back_left_joint.append([t_1[i],t_2[i],t_3[i]])\n",
    "    T_eval=T.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    x_tool_bl.append(T_eval[3])\n",
    "    y_tool_bl.append(T_eval[7])\n",
    "    z_tool_bl.append(T_eval[11])\n",
    "    J_eval=J_BL.subs([(theta_1,t_1[i]),(theta_2,t_2[i]),(theta_3,t_3[i])])\n",
    "    q=J_eval.pinv()*X_eval\n",
    "    q=q*dt\n",
    "#     print(q)\n",
    "    t_1.append(q[0]+t_1[i])\n",
    "    t_2.append(q[1]+t_2[i])\n",
    "    t_3.append(q[2]+t_3[i])\n",
    "    \n",
    "    print(\".\",end=\"\")\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "aebbe8d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a grid of subplots with 2 rows and 2 columns\n",
    "fig, axs = plt.subplots(2, 2)\n",
    "\n",
    "# Plot 1: Top left subplot\n",
    "axs[0, 0].plot(x_tool_br, z_tool_br)\n",
    "axs[0, 0].set_xlabel(\"y coordinate\")\n",
    "axs[0, 0].set_ylabel(\"z coordinate\")\n",
    "axs[0, 0].set_title(\"Trajectory of Back Right Leg\")\n",
    "axs[0, 0].axis(\"equal\")\n",
    "axs[0, 0].grid(True)\n",
    "\n",
    "# Plot 2: Top right subplot\n",
    "axs[0, 1].plot(x_tool_bl, z_tool_bl)  # Replace with your desired plot\n",
    "axs[0, 1].set_xlabel(\"y\")\n",
    "axs[0, 1].set_ylabel(\"z\")\n",
    "axs[0, 1].set_title(\"Trajectory of Back Left Leg\")\n",
    "axs[0, 1].axis(\"equal\")\n",
    "axs[0, 1].grid(True)\n",
    "# Add other formatting and plotting commands as needed\n",
    "\n",
    "# Plot 3: Bottom left subplot\n",
    "axs[1, 0].plot(x_tool_fr, z_tool_fr)  # Replace with your desired plot\n",
    "axs[1, 0].set_xlabel(\"y\")\n",
    "axs[1, 0].set_ylabel(\"z\")\n",
    "axs[1, 0].set_title(\"Trajectory of Front Right Leg\")\n",
    "axs[1, 0].axis(\"equal\")\n",
    "axs[1, 0].grid(True)\n",
    "# Add other formatting and plotting commands as needed\n",
    "\n",
    "# Plot 4: Bottom right subplot\n",
    "axs[1, 1].plot(x_tool_fl, z_tool_fl)  # Replace with your desired plot\n",
    "axs[1, 1].set_xlabel(\"y\")\n",
    "axs[1, 1].set_ylabel(\"z\")\n",
    "axs[1, 1].set_title(\"Trajectory of Front Left Leg\")\n",
    "axs[1,1].axis(\"equal\")\n",
    "axs[1,1].grid(True)\n",
    "# Add other formatting and plotting commands as needed\n",
    "\n",
    "# Adjust the layout to avoid overlapping titles and labels\n",
    "fig.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "4d3aa0e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, -0.7, 1.5],\n",
       " [-1.13099434435882e-8, -0.723013052356928, 1.50005012852982],\n",
       " [-2.27036025569893e-8, -0.745506767868113, 1.49895974526541],\n",
       " [-3.41578458478223e-8, -0.767456547736724, 1.49672837456132],\n",
       " [-4.56510898414961e-8, -0.788837639436622, 1.49335524914234],\n",
       " [-5.71634892893025e-8, -0.809625275978752, 1.48883927765579],\n",
       " [-6.86771130780500e-8, -0.829794802025259, 1.48317898930385],\n",
       " [-8.01761037630411e-8, -0.849321784288791, 1.47637245516667],\n",
       " [-9.16468195569231e-8, -0.868182103700460, 1.46841718563839],\n",
       " [-1.03077958655585e-7, -0.886352026910018, 1.45931000315011],\n",
       " [-1.14460666845659e-7, -0.903808254776668, 1.44904688902416],\n",
       " [-1.25788630452733e-7, -0.920527945595865, 1.43762280286864],\n",
       " [-1.37058157763208e-7, -0.936488710860041, 1.42503147235111],\n",
       " [-1.48268253208871e-7, -0.951668581339241, 1.41126515044915],\n",
       " [-1.59420689767320e-7, -0.966045941156322, 1.39631433631854],\n",
       " [-1.70520086233486e-7, -0.979599427280122, 1.38016745468630],\n",
       " [-1.81573997437394e-7, -0.992307791419740, 1.36281048708637],\n",
       " [-1.92593027063641e-7, -1.00414972061295, 1.34422654620035],\n",
       " [-2.03590974691761e-7, -1.01510361178262, 1.32439538189226],\n",
       " [-2.14585031290022e-7, -1.02514729408095, 1.30329280402195],\n",
       " [-2.25596040768355e-7, -1.03425769080556, 1.28089000248063],\n",
       " [-2.36648849919786e-7, -1.04241040984537, 1.25715273867673],\n",
       " [-2.47772775652591e-7, -1.04957924769494, 1.23204037426708],\n",
       " [-2.59002227859240e-7, -1.05573558661404, 1.20550469132447],\n",
       " [-2.70377539880566e-7, -1.06084765682740, 1.17748844191872],\n",
       " [-2.81946078543177e-7, -1.06487962471303, 1.14792354205137],\n",
       " [-2.93763735855976e-7, -1.06779045207924, 1.11672879155620],\n",
       " [-2.81630521484395e-7, -1.06604845590995, 1.14965063071813],\n",
       " [-2.69830284070453e-7, -1.06312037845323, 1.18082373945100],\n",
       " [-2.58290919603887e-7, -1.05905745048135, 1.21035399556909],\n",
       " [-2.46952083380637e-7, -1.05390314083025, 1.23832905926355],\n",
       " [-2.35762768850158e-7, -1.04769489271188, 1.26482188207107],\n",
       " [-2.24679549230312e-7, -1.04046546674978, 1.28989339242431],\n",
       " [-2.13665287076073e-7, -1.03224399849079, 1.31359458665707],\n",
       " [-2.02688181056773e-7, -1.02305684409380, 1.33596818290585],\n",
       " [-1.91721059954013e-7, -1.01292826551472, 1.35704994845292],\n",
       " [-1.80740860460452e-7, -1.00188099145097, 1.37686977953390],\n",
       " [-1.69728242990601e-7, -0.989936679971915, 1.39545259099092],\n",
       " [-1.58667311589769e-7, -0.977116301541459, 1.41281905800340],\n",
       " [-1.47545412223886e-7, -0.963440456022970, 1.42898624133416],\n",
       " [-1.36352989418484e-7, -0.948929633596428, 1.44396811970979],\n",
       " [-1.25083485224935e-7, -0.933604426884506, 1.45777604720404],\n",
       " [-1.13733267350855e-7, -0.917485699693539, 1.47041914920484],\n",
       " [-1.02301575540670e-7, -0.900594716431728, 1.48190466730669],\n",
       " [-9.07904768752618e-8, -0.882953235334527, 1.49223826099720],\n",
       " [-7.92048220725483e-8, -0.864583568008479, 1.50142427210103],\n",
       " [-6.75521960487313e-8, -0.845508607427312, 1.50946595646617],\n",
       " [-5.58428571361888e-8, -0.825751826322081, 1.51636568622739],\n",
       " [-4.40896604112992e-8, -0.805337247855116, 1.52212512508575],\n",
       " [-3.23079617269365e-8, -0.784289390517425, 1.52674537834979],\n",
       " [-2.05155001242394e-8, -0.762633189307401, 1.53022711895096]]"
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "front_left_joint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "cffe940f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, -0.7, 1.5],\n",
       " [2.10920844121318e-8, -0.735108227846018, 1.57793213143621],\n",
       " [4.53998094068574e-8, -0.763586679628283, 1.65220706403443],\n",
       " [7.35942694628835e-8, -0.784649426149904, 1.72211285698553],\n",
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